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Análisis Automatizado de Ecocardiografía Basado en Inteligencia Artificial y el Flujo de Trabajo de los Sonógrafos:

Akira Sakamoto¹, Nobuyuki Kagiyama¹, Eiichiro Sato¹

¹Department of Cardiovascular Biology and Medicine Juntendo University Graduate School of Medicine Tokyo Japan.

Journal of the American Heart Association

|December 17, 2025

Abstracto

Background:

This trial evaluated whether an artificial intelligence (AI)-based automatic analysis for echocardiography could improve sonographer workflow in real-world clinical practice.

Methods:

In a single-center crossover trial, 4 sonographers were randomly assigned to use AI assistance (AI days) or manual workflow (non-AI days) on a daily basis. The AI tool automatically measured echocardiographic parameters, allowing sonographers to focus on verifying AI-generated values. Expert echocardiologists finalized all reports. The primary end point was examination efficiency, measured by examination time per patient and number of examinations per day. Secondary end points included sonographer fatigue, the number of analyzed echocardiographic parameters, and image quality.

Results:

A total of 585 patients were scanned over 38 study days (AI days, 317; non-AI days, 268) between January 30 and March 26, 2024. Baseline characteristics were comparable between groups. AI assistance significantly reduced examination time (13.0±3.5 minutes versus 14.3±4.2 minutes, P<0.001) and increased the number of daily examinations (16.7±2.5 versus 14.1±2.5, P=0.003). The number of echocardiographic parameters per examination increased 3.4-fold on AI days (85±12 versus 25±1, P<0.001). Despite the higher workload, sonographers reported lower mental fatigue scores on AI days (4.1±1.1 versus 4.7±0.6, P=0.039). AI-generated measurements agreed with final expert-endorsed values within acceptable clinical limits for 90% of parameters. Notably, image quality significantly improved on AI days (P<0.001).

Conclusions:

This real-world randomized trial demonstrated that AI-based echocardiographic analysis enhances workflow efficiency, reduces sonographer fatigue, and improves image quality without compromising diagnostic integrity. AI integration holds promise for optimizing high-volume echocardiography workflows.

Registration:

URL: https://center6.umin.ac.jp. Unique identifier: UMIN000053259.

Palabras clave:

inteligencia artificial análisis automatizado ecocardiografía ensayo aleatorizado carga de trabajo

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